{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T10:58:13Z","timestamp":1756810693524,"version":"3.38.0"},"reference-count":35,"publisher":"China Science Publishing & Media Ltd.","issue":"1","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Due to the large-scale spread of COVID-19, which has a significant impact on human health and social economy, developing effective antiviral drugs for COVID-19 is vital to saving human lives. Various biomedical associations, e.g., drug-virus and viral protein-host protein interactions, can be used for building biomedical knowledge graphs. Based on these sources, large-scale knowledge reasoning algorithms can be used to predict new links between antiviral drugs and viruses. To utilize the various heterogeneous biomedical associations, we proposed a fusion strategy to integrate the results of two tensor decomposition-based models (i.e., CP-N3 and ComplEx-N3). Sufficient experiments indicated that our method obtained high performance (MRR=0.2328). Compared with CP-N3, the mean reciprocal rank (MRR) is increased by 3.3% and compared with ComplEx-N3, the MRR is increased by 3.5%. Meanwhile, we explored the relationship between the performance and relationship types, which indicated that there is a negative correlation (PCC=0.446, P-value=2.26e-194) between the performance of triples predicted by our method and edge betweenness.<\/jats:p>","DOI":"10.1162\/dint_a_00117","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T20:35:43Z","timestamp":1642710943000},"page":"134-148","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":8,"title":["Link Prediction based on Tensor Decomposition for the Knowledge Graph\n                    of COVID-19 Antiviral Drug"],"prefix":"10.3724","volume":"4","author":[{"given":"Ting","family":"Jia","sequence":"first","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxia","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuo","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"},{"name":"BNRIST\/Department of Automation, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuezhong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2026","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"volume-title":"Knowledge graph embedding for link prediction: A comparative\n                        analysis","year":"2020","author":"Rossi","key":"2022020307030009900_ref1"},{"issue":"12","key":"2022020307030009900_ref2","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and\n                        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            Conference on Neural Information Processing Systems-Volume","author":"Bordes","year":"2013"},{"volume-title":"Rotate: Knowledge graph embedding by relational rotation in complex\n                        space","year":"2019","author":"Sun","key":"2022020307030009900_ref20"},{"volume-title":"Rnnlogic: Learning logic rules for reasoning on knowledge\n                        graphs","year":"2020","author":"Qu","key":"2022020307030009900_ref21"},{"volume-title":"Go for a walk and arrive at the answer: Reasoning over paths in\n                        knowledge bases using reinforcement learning","year":"2017","author":"Das","key":"2022020307030009900_ref22"},{"key":"2022020307030009900_ref23","first-page":"460","volume-title":"STransE: A novel embedding model of entities and relationships in\n                        knowledge bases","author":"Nguyen","year":"2016"},{"volume-title":"Convolutional 2d knowledge graph 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Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/dint\/article-pdf\/4\/1\/134\/1985051\/dint_a_00117.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/dint\/article-pdf\/4\/1\/134\/1985051\/dint_a_00117.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T07:42:34Z","timestamp":1741938154000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciengine.com\/doi\/10.1162\/dint_a_00117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2,3]]}},"URL":"https:\/\/doi.org\/10.1162\/dint_a_00117","relation":{},"ISSN":["2641-435X"],"issn-type":[{"type":"electronic","value":"2641-435X"}],"subject":[],"published-other":{"date-parts":[[2022]]},"published":{"date-parts":[[2022]]}}}